Setup notebook

library(tidyverse)

Load data

AUC range

aha_pred %>%
  select(auc) %>%
  distinct() %>%
  ggplot()+
  geom_boxplot(aes(y= auc, x= "AHA"))+
  geom_point(aes(y= auc, x= "AHA"), alpha = 0.5)+
  labs(title = "AHA AUC range")+
  theme_classic()


pomms_pred %>%
  select(auc) %>%
  distinct() %>%
  ggplot()+
  geom_boxplot(aes(y= auc, x= "POMMS"))+
  geom_point(aes(y= auc, x= "POMMS"), alpha = 0.5)+
  labs(title = "POMMS AUC range")+
  theme_classic()

Best AUC

max = aha_pred$auc %>%
  max()

aha_pred %>%
  filter(auc == max) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()


aha_pred %>%
  filter(auc == 0.94) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()

max = pomms_pred$auc %>%
  max()

pomms_pred %>%
  filter(auc == max) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()


pomms_pred %>%
  filter(auc == 0.82) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()

#AUCs above 0.7

aha_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()


aha_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()

pomms_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()


pomms_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()

ASV lookup table

CHOICE

Best AUC importances

max = aha_pred$auc %>%
  max()

#colnames(aha_importance)

importance_summary = aha_importance %>%
  filter(auc == max) %>%
  pivot_longer(cols = c("ATCCTATTATTTTATTATTTTACGAAACTAAACAAAGGTTCAGCAAGCGAGAATAATAAAAAAAG":"CATAAACTGGTAGCGACCGGCACCACCGGTAAACTGATCGCAGAAGCTACCGGCT"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))
`summarise()` has grouped output by 'label'. You can override using the `.groups` argument.
ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")


name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
 [1] "cocoa bean"                                                                                                                   
 [2] "avocado, NA, NA, NA, NA, NA"                                                                                                  
 [3] "NA, ceylon cinnamon, bay leaf, avocado, NA, NA"                                                                               
 [4] "goji berry, NA, goji berry, cutleaf groundcherry, tomatillo, nightshade, NA, blackberry nightshade, potato, NA"               
 [5] "garlic"                                                                                                                       
 [6] "muscadine grape, common grape vine, NA, NA"                                                                                   
 [7] "common grape vine"                                                                                                            
 [8] "common grape vine"                                                                                                            
 [9] "black chokeberry, apple, NA, crab apple, european pear, NA, afghan pear, nashi pear, siberian pear, hybrid chinese white pear"
[10] "NA, pecan"                                                                                                                    
[11] "sesame"                                                                                                                       
max = pomms_pred$auc %>%
  max()

#colnames(pomms_importance)

importance_summary = pomms_importance %>%
  filter(auc == max) %>%
  pivot_longer(cols = c("ATCACGTTTTCCGAAAACAAACAAAGGTTCAGAAAGCGAAAATAAAAAAG":"ATCCTGTTTTCTCAAAACAAACAAAGGTTCAGAAAAAAAG"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))
`summarise()` has grouped output by 'label'. You can override using the `.groups` argument.
ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")


name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
 [1] "dandelion"                                                                                                                                                                                                                                             
 [2] "sunflower, jerusalem artichoke, sunchoke, dandelion"                                                                                                                                                                                                   
 [3] "ashanti pepper, long pepper, black pepper, wild betel"                                                                                                                                                                                                 
 [4] "canola, rapeseed"                                                                                                                                                                                                                                      
 [5] "brown mustard, canola, rapeseed, black mustard, cabbage, broccoli, cauliflower, kale, Brussels sprouts, collard greens, savoy, kohlrabi, gai lan, mustards, bok choy, canola, rapeseed, field mustard, napa cabbage, turnip, rocket, NA, white mustard"
 [6] "oregano, NA, thyme"                                                                                                                                                                                                                                    
 [7] "sage"                                                                                                                                                                                                                                                  
 [8] "sage"                                                                                                                                                                                                                                                  
 [9] "NA, NA, rosemary"                                                                                                                                                                                                                                      
[10] "lemon balm, rosemary"                                                                                                                                                                                                                                  
[11] "sesame"                                                                                                                                                                                                                                                
[12] NA                                                                                                                                                                                                                                                      
[13] NA                                                                                                                                                                                                                                                      
[14] "NA, NA"                                                                                                                                                                                                                                                
[15] NA                                                                                                                                                                                                                                                      
[16] "rye, NA, NA, NA, NA, NA, bread wheat, NA, NA, NA, emmer wheat, spelt, einkorn, wild einkorn, spelt, NA, domesticated hulled wheat, NA, rivet wheat, durum wheat, wild einkorn, NA"                                                                     
[17] "coconut"                                                                                                                                                                                                                                               
[18] "fennel, dill, angelica, cumin, wild carrot, parsnip, parsley"                                                                                                                                                                                          
[19] "lettuce"                                                                                                                                                                                                                                               
[20] "flaxseed"                                                                                                                                                                                                                                              
[21] "tomato, NA"                                                                                                                                                                                                                                            

AUC above 0.7 importances

#colnames(aha_importance)

importance_summary = aha_importance %>%
  filter(auc >0.7) %>%
  pivot_longer(cols = c("ATCCTATTATTTTATTATTTTACGAAACTAAACAAAGGTTCAGCAAGCGAGAATAATAAAAAAAG":"CATAAACTGGTAGCGACCGGCACCACCGGTAAACTGATCGCAGAAGCTACCGGCT"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))
`summarise()` has grouped output by 'label'. You can override using the `.groups` argument.
ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")


name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
 [1] "cocoa bean"                                                                                                                   
 [2] "avocado, NA, NA, NA, NA, NA"                                                                                                  
 [3] "NA, ceylon cinnamon, bay leaf, avocado, NA, NA"                                                                               
 [4] "garlic"                                                                                                                       
 [5] "goji berry, NA, goji berry, cutleaf groundcherry, tomatillo, nightshade, NA, blackberry nightshade, potato, NA"               
 [6] "muscadine grape, common grape vine, NA, NA"                                                                                   
 [7] "common grape vine"                                                                                                            
 [8] "common grape vine"                                                                                                            
 [9] "sesame"                                                                                                                       
[10] "NA, pecan"                                                                                                                    
[11] "black chokeberry, apple, NA, crab apple, european pear, NA, afghan pear, nashi pear, siberian pear, hybrid chinese white pear"
importance_summary = pomms_importance %>%
  filter(auc >0.7) %>%
  pivot_longer(cols = c("ATCACGTTTTCCGAAAACAAACAAAGGTTCAGAAAGCGAAAATAAAAAAG":"ATCCTGTTTTCTCAAAACAAACAAAGGTTCAGAAAAAAAG"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))
`summarise()` has grouped output by 'label'. You can override using the `.groups` argument.
ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")


name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
 [1] "dandelion"                                                                                                                                                                                                                                             
 [2] "sunflower, jerusalem artichoke, sunchoke, dandelion"                                                                                                                                                                                                   
 [3] "tea"                                                                                                                                                                                                                                                   
 [4] "ashanti pepper, long pepper, black pepper, wild betel"                                                                                                                                                                                                 
 [5] "canola, rapeseed"                                                                                                                                                                                                                                      
 [6] "brown mustard, canola, rapeseed, black mustard, cabbage, broccoli, cauliflower, kale, Brussels sprouts, collard greens, savoy, kohlrabi, gai lan, mustards, bok choy, canola, rapeseed, field mustard, napa cabbage, turnip, rocket, NA, white mustard"
 [7] "rye, NA, NA, NA, NA, NA, bread wheat, NA, NA, NA, emmer wheat, spelt, einkorn, wild einkorn, spelt, NA, domesticated hulled wheat, NA, rivet wheat, durum wheat, wild einkorn, NA"                                                                     
 [8] "fennel, dill, angelica, cumin, wild carrot, parsnip, parsley"                                                                                                                                                                                          
 [9] "goji berry, NA, goji berry, cutleaf groundcherry, tomatillo, nightshade, NA, blackberry nightshade, potato, NA"                                                                                                                                        
[10] "coconut"                                                                                                                                                                                                                                               
[11] "lettuce"                                                                                                                                                                                                                                               
[12] "oat"                                                                                                                                                                                                                                                   
---
title: "R Notebook"
output: html_notebook
---

# Setup notebook

```{r}
library(tidyverse)
```

# Load data

```{r}
aha_pred = read_csv("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/Machine_learning/LAD_lab_ML/code/aha_to_choice_predictions.csv")

aha_importance = read_csv("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/Machine_learning/LAD_lab_ML/code/aha_to_choice_feature_importance.csv")

pomms_pred = read_csv("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/Machine_learning/LAD_lab_ML/code/pomms_to_choice_predictions.csv")

pomms_importance = read_csv("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/Machine_learning/LAD_lab_ML/code/pomms_to_choice_feature_importance.csv")
```
# AUC range

```{r}
aha_pred %>%
  select(auc) %>%
  distinct() %>%
  ggplot()+
  geom_boxplot(aes(y= auc, x= "AHA"))+
  geom_point(aes(y= auc, x= "AHA"), alpha = 0.5)+
  labs(title = "AHA AUC range")+
  theme_classic()

pomms_pred %>%
  select(auc) %>%
  distinct() %>%
  ggplot()+
  geom_boxplot(aes(y= auc, x= "POMMS"))+
  geom_point(aes(y= auc, x= "POMMS"), alpha = 0.5)+
  labs(title = "POMMS AUC range")+
  theme_classic()
```

# Best AUC
```{r}
max = aha_pred$auc %>%
  max()

aha_pred %>%
  filter(auc == max) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()

aha_pred %>%
  filter(auc == 0.94) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()
```

```{r}
max = pomms_pred$auc %>%
  max()

pomms_pred %>%
  filter(auc == max) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()

pomms_pred %>%
  filter(auc == 0.82) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()
```
#AUCs above 0.7

```{r}
aha_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()

aha_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="AHA to CHOICE")+
  theme_classic()
```


```{r}
pomms_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Case, colour = timepoint))+
  geom_point(aes(x= treatment, y= Case, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()

pomms_pred %>%
  filter(auc > 0.7) %>%
  ggplot()+
  geom_boxplot(aes(x= treatment, y= Control, colour = timepoint))+
  geom_point(aes(x= treatment, y= Control, colour = timepoint), position=position_jitterdodge(), alpha = 0.2)+
  labs(title ="POMMS to CHOICE")+
  theme_classic()
```

# ASV lookup table

```{r}
# combined = readRDS("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/CHOICE/All_cohorts/data_objects/CHOICE_POMMS_AHA_combined_20221213.rds")
# 
# lookup = combined %>%
#   tax_table() %>%
#   as.data.frame() %>%
#   as_tibble(rownames = NA) %>%
#   rownames_to_column("asv")
```

```{r}
common_names = read_csv("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Brianna P/diet/food-dbs/data/outputs/dada2-compatible/trnL/trnLGH ASV common names.csv")
```


# CHOICE

```{r}
c_ps = readRDS("/Users/aa370/Library/CloudStorage/Box-Box/project_davidlab/LAD_LAB_Personnel/Ammara_A/Projects/CHOICE/CHOICE_Trnl/CHOICE_20220912/ps_objects/choice_complete_20221205_clean.rds")

otu_clr = abundances(c_ps, "clr") %>% # clr transform
  t()

c_ps = phyloseq(otu_table(otu_clr, taxa_are_rows = FALSE),
                   sample_data(sample_data(c_ps)),
                   tax_table(tax_table(c_ps)))
samdf = c_ps %>%
  sample_data() %>%
  as.data.frame() %>%
  as_tibble(rownames = NA) %>%
  rownames_to_column("sample") %>%
  select(sample, id, treatment, true_week, timepoint)

otudf = c_ps %>%
  otu_table() %>%
  as.data.frame() %>%
  as_tibble(rownames = NA) %>%
  rownames_to_column("sample")

input = samdf %>%
  left_join(otudf)
```
# Best AUC importances
```{r}
max = aha_pred$auc %>%
  max()

#colnames(aha_importance)

importance_summary = aha_importance %>%
  filter(auc == max) %>%
  pivot_longer(cols = c("ATCCTATTATTTTATTATTTTACGAAACTAAACAAAGGTTCAGCAAGCGAGAATAATAAAAAAAG":"CATAAACTGGTAGCGACCGGCACCACCGGTAAACTGATCGCAGAAGCTACCGGCT"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))

ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")

name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
```

```{r}
max = pomms_pred$auc %>%
  max()

#colnames(pomms_importance)

importance_summary = pomms_importance %>%
  filter(auc == max) %>%
  pivot_longer(cols = c("ATCACGTTTTCCGAAAACAAACAAAGGTTCAGAAAGCGAAAATAAAAAAG":"ATCCTGTTTTCTCAAAACAAACAAAGGTTCAGAAAAAAAG"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))

ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")

name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
```

# AUC above 0.7 importances
```{r}
#colnames(aha_importance)

importance_summary = aha_importance %>%
  filter(auc >0.7) %>%
  pivot_longer(cols = c("ATCCTATTATTTTATTATTTTACGAAACTAAACAAAGGTTCAGCAAGCGAGAATAATAAAAAAAG":"CATAAACTGGTAGCGACCGGCACCACCGGTAAACTGATCGCAGAAGCTACCGGCT"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))

ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")

name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
```

```{r}
importance_summary = pomms_importance %>%
  filter(auc >0.7) %>%
  pivot_longer(cols = c("ATCACGTTTTCCGAAAACAAACAAAGGTTCAGAAAGCGAAAATAAAAAAG":"ATCCTGTTTTCTCAAAACAAACAAAGGTTCAGAAAAAAAG"), names_to = "food", values_to = "importance") %>%
  group_by(label, food) %>%
  summarise(mean_importance = mean(importance)) %>%
  arrange(label, desc(mean_importance))

ranking = importance_summary %>%
  pull(food)

top10 = ranking[1:10]

plot_food_direction(top10, input, "treatment")

name_list = c()
for(item in top10){
 name = common_names %>%
    filter(str_detect(asv, item)) %>%
    pull(common_name)
 name_list = append(name_list, name)
}
name_list
```


